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X*x*x Is Equal To 2 5 Meter Download Link Video Youtube Video


this introduces the problem of Same-origin policy. Essentially, you cannot download this file from www.youtube.com because they are different domains. A workaround of this problem is CORS. With CORS, s.ytimg.com could add this header


and it would allow the JavaScript to download from www.youtube.com. Of course they do not do this. A workaround for this workaround is to use a CORS proxy. This is a proxy that responds with the following header to all requests




x*x*x is equal to 2 5 meter download link video youtube video



According to the most recent 2019 definition, the meter is defined as the distance traveled by light in vacuum during a time interval with a duration of 1/299,792,458 of a second.[1] One meter is equal to 100 centimeters, 3.28084 feet, or 39.37 inches.


The foot is a unit of length measurement equal to 12 inches or 1/3 of a yard.Because the international yard is legally defined to be equal to exactly 0.9144 meters, one foot is equal to 0.3048 meters.[2]


The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database (NLCD). Thus, the USDA, NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.The training and validation data used to create and accuracy assess the CDL has traditionally been based on ground truth data that is buffered inward 30 meters. This was done 1) because satellite imagery (as well as the polygon reference data) in the past was not georeferenced to the same precision as now (i.e. everything "stacked" less perfectly), 2) to eliminate from training spectrally-mixed pixels at land cover boundaries, and 3) to be spatially conservative during the era when coarser 56 meter AWiFS satellite imagery was incorporated. Ultimately, all of these scenarios created "blurry" edge pixels through the seasonal time series which it was found if ignored from training in the classification helped improve the quality of CDL. However, the accuracy assessment portion of the analysis also used buffered data meaning those same edge pixels were not assessed fully with the rest of the classification. This would be inconsequential if those edge pixels were similar in nature to the rest of the scene but they are not as they tend to be more difficult to classify correctly. Thus, the accuracy assessments as have been presented are inflated somewhat.Beginning with the 2016 CDLs we published both the traditional "buffered" accuracy metrics and the new "unbuffered" accuracy assessments. The purpose of publishing both versions is to provide a benchmark for users interested in comparing the different validation methods. For the 2017 CDL season we are now only publishing the unbuffered accuracy assessments within the official metadata files and offer the full "unbuffered" error matrices for download on this FAQs webpage. We plan to continue producing these unbuffered accuracy assessments for future CDLs. However, there are no plans to create these unbuffered accuracy assessments for past years. It should be noted that accuracy assessment is challenging and the CDL group has always strived to provide robust metrics of usability to the land cover community. This admission of modestly inflated accuracy measures does not render past assessments useless. They were all done consistently so comparison across years and/or states is still valid. Yet, by now providing both scenarios for 2016 gives guidance on the bias.The full error matrices are included in the downloadable links below.


To create a pixel "count" field in the attribute table of the downloaded CDL use the "Build Raster Attribute Table" Function in ESRI ArcGIS. In ESRI ArcGIS Version 9.3 and 10 this function is located at ArcToolbox > Data Management Tools > Raster > Raster Properties > Build Raster Attribute Table. Specify the downloaded CDL tif file as the Input Raster and accept all other defaults and click OK. After it has run successfully, a new "Count" data field is added to the attribute table. Count represents a raw pixel count. To calculate acreage multiply the count by the square meters conversion factor which is dependent upon the CDL pixel size. The conversion factor for 30 meter pixels is 0.222394. The conversion factor for 56 meter pixels is 0.774922.


CropScape allows users to analyze and interact with areas less than 2,000,000 square kilometers. However, users can download the entire national CDL by year by following the instructions in this FAQ question in this FAQ question (Click Here) which could then be used to perform analysis using their own GIS or image processing software.


If you receive a "HTTP Error 500", then check that you are using a static link rather than dynamic. If you are using BOX then this link may help with creating a static link ( -Forum/How-to-mass-download-Static-Share-Links/td-p/11973).


CropScape data can be exported to a KML format that is downloaded to your local drive. This KML file can then be used in Google Earth if desired. Instructions for how to download data and specify the KML format are detailed in the Section 3.e.ii of the Help hyperlink in the upper righthand corner of the CropScape webpage. For Bing Maps you will need to write JavaScript code using the Bing Map API to add a KML layer, please follow the instructions at: -us/library/cc316942.aspx.


Below are instructions for downloading from the NRCS Geospatial Data Gateway ( ). Recently some users are reporting problems with retrieving all years of CDL data when downloading from the Geospatial Data Gateway. If the instructions below do not work or you do not receive the most recent years of CDL data then try using the 'Direct Data Download' link in the lower right-hand corner of their webpage. 1. Go to the website: 2. Click on the "Get Data" button on the upper right hand side of the page 3. Select the state of interest from the drop down list 4. Select a single county. Your download will include the entire state regardless of what county you select 5. Click on "Submit Selected Counties" 6. Place a checkmark next to "Cropland Data Layer by State" under the "Land Use Land Cover" category. Click continue 7. Select "FTP" from the "Delivery" category. Click continue 8. Fill out all fields marked with a "*" 9. Click continue 10. Review order for accuracy then click "Place Order" 11. Note your order number. You will receive an email with a link to download your order.


The CDL Program uses medium spatial resolution (30 meter) satellite imagery. Currently, it is too costly to use higher resolution satellites to perform crop acreage estimation over large areas. The current CDL Program uses the Landsat 8 and 9 OLI/TIRS sensor, the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2, the ISRO ResourceSat-2 LISS-3, and the ESA SENTINEL-2 A and B sensors. Imagery is downloaded daily with the objective of obtaining at least one cloud-free usable image every two weeks throughout the growing season.


Detailed accuracy assessment tables are published within the official metadata files. Generally, the large area row crops have producer accuracies ranging from mid 80% to mid 90%. The full error matrices used to create the accuracy assessment information contained within the metadata files is available for download in Question 11 and 54 of this FAQs webpage. NOTE ABOUT THE UNBUFFERED VALIDATION ACCURACY TABLES BEGINNING IN 2016: The training and validation data used to create and accuracy assess the CDL has traditionally been based on ground truth data that is buffered inward 30 meters. This was done 1) because satellite imagery (as well as the polygon reference data) in the past was not georeferenced to the same precision as now (i.e. everything "stacked" less perfectly), 2) to eliminate from training spectrally-mixed pixels at land cover boundaries, and 3) to be spatially conservative during the era when coarser 56 meter AWiFS satellite imagery was incorporated. Ultimately, all of these scenarios created "blurry" edge pixels through the seasonal time series which it was found if ignored from training in the classification helped improve the quality of CDL. However, the accuracy assessment portion of the analysis also used buffered data meaning those same edge pixels were not assessed fully with the rest of the classification. This would be inconsequential if those edge pixels were similar in nature to the rest of the scene but they are not- they tend to be more difficult to classify correctly. Thus, the accuracy assessments as have been presented are inflated somewhat. Beginning with the 2016 CDL season we are creating CDL accuracy assessments using unbuffered validation data. These "unbuffered" accuracy metrics will now reflect the accuracy of field edges which have not been represented previously. Beginning with the 2016 CDLs we published both the traditional "buffered" accuracy metrics and the new "unbuffered" accuracy assessments. The purpose of publishing both versions is to provide a benchmark for users interested in comparing the different validation methods. For the 2017 CDL season we are now only publishing the unbuffered accuracy assessments within the official metadata files and offer the full "unbuffered" error matrices for download on the FAQs webpage. We plan to continue producing these unbuffered accuracy assessments for future CDLs. However, there are no plans to create these unbuffered accuracy assessments for past years. It should be noted that accuracy assessment is challenging and the CDL group has always strived to provide robust metrics of usability to the land cover community. This admission of modestly inflated accuracy measures does not render past assessments useless. They were all done consistently so comparison across years and/or states is still valid. Yet, by now providing both scenarios for 2016 gives guidance on the bias.


Prior to 2006, the Landsat TM/ETM categorized images were co-registered to MDA/EarthSat Inc's ortho-rectified GeoCover Stock Mosaic images using automated block correlation techniques. The resulting correlations were applied to each categorized image and then added to a master image or mosaic using NASS' in-house software, PEDITOR. The GeoCover Stock Mosaics are within 50 meters root mean squared error overall. Newer Cropland Data Layers (2006 to current) retain the spatial attributes of the input imagery. The AWiFS imagery has a positional accuracy of 60 meters at the circular error at the 90 percent confidence level (CE90). CE90 is a standard metric often used for horizontal accuracy in map products and can be interpreted as 90% of well-defined points tested must fall within a certain radial AWiFS distance. The Landsat 4/5/8 imagery is obtained via download from the USGS Global Visualization Viewer (Glovis) website ( ). Please reference the metadata on the Glovis website for each Landsat scene for positional accuracy. The majority of the Landsat data is available at Level 1T (precision and terrain corrected). The DEIMOS-1 and DMC-UK 2 imagery used in the production of the Cropland Data Layer is orthorectified to a radial root mean square error (RMSE) of approximately 10 meters. More information about the geo-positional accuracy of the ESA Sentinel-2 imagery can be found at More information about the geo-positional accuracy of the ISRO ResourceSat-2 LISS-3 imagery can be found on page 104 of the ResourceSat2 Handbook at _data_user_handbook.pdf.


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